The Optimal Design for PEMFC Modeling Based on BPNN and Taguchi Method

Abstract

This article has presented a new approach to estimate the output voltage of proton exchange membrane fuel cell (PEMFC) accurately by combining the use of a back-propagation neural network (BPNN) model and the Taguchi method. Using the PEMFC experimental data measured from performance test equipment of PEMFC, the BPNN model could be trained and constructed for obtaining the steady state output voltage of PEMFC. Furthermore, in order to determine the important parameters in BPNN, the Taguchi method is used for parameter optimization, with the goal of reducing the estimation error. The test equipment of PEMFC is accurate enough for acquiring the output voltage of PEMFC, and is quite useful for teaching purpose. However, taking the high cost, complicated operation procedure, and environment safety into consideration, it is necessary to develop a simulation model of PEMFC to benefit teaching and R&D. Therefore, this article will present an approach for constructing a BPNN model with precise accuracy for the output voltage of PEMFC. For achieving the BPNN model with high precision, a troublesome work has to be taken care of, that is, to determine all the parameters required in BPNN. We will introduce Taguchi method to solve this problem as well. Finally, to show the superiority of the proposed model, this approach has compared the estimation values of output voltage for PEMFC from BPNN model without using Taguchi method. One can easily find that the error of the proposed method is much smaller than that of the BPNN model without Taguchi method; that is, the proposed approach has better performance on estimation for PEMFC output voltages.